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Know more about Open source Datasets on the use of AI over Infrared Images for Medical Applications at https://lnkd.in/g8_3ZfRb If you are working in the field of AI over Infrared Images for Medical Applications, Submit your latest research work to AIIIMA 2024 at https://lnkd.in/gEwdjKxr Why should you consider submitting to AIIIMA 2024? 1. Get valuable feedback and reviews from esteemed researchers in the field of AIIIMA. 2. No Publication/Registration Fees: If your paper gets accepted, it will be featured in the AIIIMA proceedings without any charges. AIIIMA has also waived the registration fee to encourage more submissions. 3. Your manuscript will be part of AIIIMA proceeding published by Springer's Lecture Notes in Computer Science (LNCS) 4. AIIIMA'24 is a fully online conference (No travel costs). Important Dates: 30 July 2024 Paper submissions due 30 August 2024 Notification of paper decisions 15 September 2024 Camera ready papers due 09 November 2024 AIIIMA conference date

🚨 Exciting Announcement: Special Issue on AI in Biomedical Imaging and Signal Processing! We are thrilled to share our new call for papers on Artificial Intelligence in Biomedical Imaging and Signal Processing. With recent advances in deep learning and high-performance computing (HPC), AI is transforming disease diagnosis using medical images like X-ray, CT, MRI, and more. However, challenges remain, including the need for better image quality, segmentation, and multimodal data fusion. Researchers are invited to submit their work on topics such as AI-assisted detection, segmentation, and ethics in AI for diagnostics. Guest Editors: # Olusola Abayomi-Alli, Ph.D. # Olamilekan Shobayo(AFHEA) # Modupe Odusami Submission link: https://lnkd.in/eipN9A85 Submit now! #AIinHealthcare #MedicalImaging #DeepLearning #BiomedicalAI #MachineLearning #Diagnostics #AI #Diagnostics MDPI

I am thrilled to announce that our paper, "Generative Adversarial Networks in Medical Image Analysis: A Comprehensive Survey", has been officially published by Springer as part of the ICICC 2024 proceedings! 🚀✨ This paper explores the revolutionary role of Generative Adversarial Networks (GANs) in the field of medical imaging, highlighting how GANs are pushing the boundaries of diagnostic technologies and enhancing various applications such as: 🔹 Medical Image Segmentation – Accurately identifying and outlining structures like organs or tumors in medical images 🔹 Image Classification – Improving diagnostic precision by classifying medical conditions based on advanced imaging data 🔹 Image Reconstruction – Reconstructing clearer, high-quality images from incomplete or noisy medical data 🔹 Image Synthesis – Generating lifelike medical images that closely mimic real patient data, vital for training models and overcoming data scarcity 🔹 Noise Reduction – Enhancing the clarity and usability of medical images by reducing artifacts and improving quality 🔹 Anomaly Detection – Assisting physicians in identifying minute abnormalities in medical images for early disease detection Through these applications, GANs hold immense promise for improving disease detection, overcoming data limitations, and tailoring treatment approaches by generating highly authentic images that mirror real patient data while ensuring privacy protection. Moreover, the paper highlights the role of advanced deep learning techniques such as Conditional GANs (cGANs) for controlled image generation, CycleGANs for image-to-image translation, and Deep Convolutional GANs (DCGANs) to enhance the quality of generated images. These deep learning methods are essential in improving the accuracy and efficiency of medical image analysis and clinical decision-making. The paper also outlines future research directions, encouraging exploration of even more innovative uses of GANs in medical imaging to advance clinical practice and medical research to new heights. This achievement would not have been possible without the incredible support of my co-authors, Kancharagunta Kishan Babu and Sreeja Nukarapu. I'm proud of what we’ve accomplished together and excited to see where this research leads next! Check out the full paper here: https://lnkd.in/g29GXrFD #MedicalImaging #AI #GenerativeAdversarialNetworks #MedicalTechnology #HealthcareInnovation #ArtificialIntelligence #DataScience #Research #DeepLearning

1/ Our paper titled "Synthetic data in radiological imaging: Current state and future outlook" was recently accepted to the British Journal of Radiology (BJR), Artificial Intelligence. This is a joint work with Andreu Badal Jana Delfino Miguel Ángel Lago Ángel Brandon J. Nelson Niloufar Saharkhiz, PhD Berkman Sahiner Ghada Zamzmi aldo badano Link: https://lnkd.in/eFpJnta4 Synthetic data has many uses and great potential to enhance radiological AI applications. Synthetic data in radiology can be generated using statistical generative, physics-based modelling, or physics-informed models. Synthetic data can be used to mitigate various limitations of patient-based datasets, reducing associated risks, data acquisition time, cost, and complexity, and mitigating existing data imbalances. Overall, the choice of how to generate synthetic data depends on the availability and type of source of evidence used to create synthetic data, available biological or physics knowledge. Also, it is crucial to identify the patient data distribution the synthetic data is intended to represent.   #fda #regulatoryscience #osel #cdrh

Image augmentation is an essential component of training deep learning models for computer vision. But the exact types of augmentation that are best and their hyperparameters can vary from one dataset to the next. And selecting them manually is both compute and time-intensive. Khrystyna Faryna et al. explored approaches for automatically selecting the image augmentation hyperparameters for H&E histology. They evaluated 4 different automatic augmentation methods: population-based augmentation (PBA), fast autoaugment, RandAugment, and differentiable automatic data augmentation (DADA). To test these approaches, they compared them on tumor metastasis detection (CAMELYON17) and breast cancer tissue type classification (TIGER), evaluating performance on slides from a different institution to test generalizability. All methods significantly outperformed a baseline of no augmentation and were mostly on par with manually tuned hyperparameters. However, the 4 methods differ in their computational complexity and ease of use. RandAugment achieved slightly better results than the rest (and is the simplest to set up) but has a much greater computational cost. They also observed that domain-specific augmentations like color augmentation that simulate staining variations were important. Given these insights, it's important to consider the properties of a particular dataset, as well as the value of computational efficiency and set-up complexity in selecting an automatic augmentation approach. https://lnkd.in/dZ9yvQJU To learn more about the latest research, subscribe to my Computer Vision Insights newsletter: https://lnkd.in/g9bSuQDP #Pathology #CancerResearch #PrecisionMedicine #MedicalImaging #MachineLearning #DeepLearning #ComputerVision

Are you going to #PathUpdate2024? I'm presenting: 'Digital Pathology: from Analog-to-Digital' Many laboratories around the world have taken on the significant challenge to transition to Digital Pathology (DP). Add to the complexity of DP are topics such as Artificial Intelligence (AI), Machine Learning (ML), Convoluted Neural Networks (CNN), sponsored content, funded studies, vested interests, multitudes of opinions etc. and it almost seems that the overwhelmed feeling when delving into this area is by design. There is no doubt that DP is a disruptive technology but there are recurring themes and issues with every implementation that we can learn from. In this presentation, I examine the challenges, pros and cons of DP implementation at the laboratory, scientists/lab staff, Information Technology and pathologists levels. Topics discussed include: - Pathology vs Radiology differences: file sizes, pixel differences, file standardisation - IT hardware: scanners, monitors, network speed, the cost-benefit fallacy - Pre-analytical (pre-scan): cut-up/grossing, slide preparation, common errors - Analytical (scan): scan errors, pathologists concordance, image interpretation issues - Post-analytical issues (post-scan): AI challenges, Australia’s digital conundrum Join me at the The Royal College of Pathologists of Australasia (RCPA) Pathology Update in Adelaide in 2024. Travis is co-host of This Medical Life podcast, freely available on all podcasting services and Spotify. #pathology #pathologists #digitalpathology #histopathology #AnswersinAdelaide #RCPA

🧠💡 News in #MRIResearch: Introducing "DeepN4", a deep learning approach that reimagines the way we correct for bias fields in T1-weighted MRI images! Check out Praitayini Kanakaraj's paper in Neuroinformatics! 🔬 T1-weighted MRI is a critical structural imaging modality, but comes with its challenges, such as low-frequency intensity artifacts due to magnetic field inhomogeneities. Correcting these biases is crucial for consistent image interpretation, and the current best practice is the N4ITK bias field correction. However, N4ITK has its limitations, including difficulties in portability and a lack of transparency for machine learning optimization. ✨ Enter DeepN4: Developed to overcome these hurdles, DeepN4 is a deep learning network trained on images from 72 different scanners across diverse age groups, using N4ITK-corrected images as a teaching guide. The result? A method that not only matches the N4ITK correction capabilities but also offers flexibility and portability across various computing platforms. 🚀 The best part? DeepN4 is designed to be fully differentiable, allowing for seamless integration and optimization within different research pipelines and workflows. DeepN4 will be integrated in the next #DiPy release. This innovation marks a significant leap forward, offering researchers an adaptable and transparent tool for MRI image preprocessing. 🔗 For more details, access to all code, and models, visit: https://lnkd.in/eYhgr4rk Dive into the future of MRI preprocessing with #DeepN4! 🧠🖥️ #DeepLearning #MRIpreprocessing #Harmonization Praitayini Kanakaraj, Tianyuan Yao, Leon Y. Cai, Ho Hin Lee, Nancy Newlin, Michael E. Kim, Chenyu Gao, Kimberly R. Pechman, Derek Archer, Timothy Hohman, Angela Jefferson, Lori L. Beason-Held, Susan M. Resnick, The Alzheimer’s Disease Neuroimaging Initiative (ADNI), The BIOCARD Study Team, Eleftherios Garyfallidis, Adam Anderson, Kurt G. Schilling, Bennett A. Landman, Daniel Moyer. "DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images". Neuroinformatics 2023. (In press)

🔍 Redefining the Future of Stroke Detection: A Look into My Recent Research! 🔍 🌟 I'm ecstatic to announce the publication of my research paper, "A Comprehensive Review and Experimental Comparison of Deep Learning Methods for Automated Hemorrhage Detection," in the esteemed Engineering Applications of Artificial Intelligence (EAAI) journal. 🧠 Hemorrhagic strokes are urgent medical crises demanding swift and accurate diagnosis. 🚀 This study involved a systematic review and comprehensive gap analysis of various deep learning approaches for automating hemorrhagic stroke identification. 🔬 Our research stands out for its meticulous examination of existing literature, the careful selection of high-performing deep learning models, and the rigorous experimentation using publicly available dataset. 🌐 By evaluating these models in a tapestry of geographically diverse datasets, we crafted a comprehensive picture, revealing hidden biases and ensuring their adaptability across diverse landscapes. 🌀 From a clinical perspective, it's crucial for the algorithm to effectively identify stroke based on appearance and complexity, rather than solely focusing on easy-to-spot cases. A biased test set towards such cases can inflate performance metrics. 🔑 This study illuminates the current landscape of automated hemorrhagic stroke identification, offering insights that not only provide a blueprint for future research and development but also debunk myths about model robustness by revealing potential biases and inaccuracies. 📚 Dive into the full paper https://lnkd.in/gHMUbRTs to discover the future of automated medical imaging analysis. The journey has been quite long, and I'm grateful to Dr. Jeny Rajan for his unwavering support and motivation as my guide. #MedicalImaging #DeepLearning #HealthcareInnovation #StrokeDetection #ResearchBreakthrough #AIinHealthcare #Neuroimaging #MachineLearning #HealthTech #DataScience

Just wrapped up an extremely insightful chat with Professor Andrew Janowczyk on my show, Saurish Speaks! I'm incredibly grateful to Professor Janowczyk for sharing his expertise on AI, ML, Deep Learning, Digital Pathology, Histology, and Computer-Aided Diagnostics (CAD). Our conversation explored the impact of AI on disease diagnosis, the future of digital pathology, the challenges in developing healthcare AI tools, and the critical importance of ethics and safety. Professor Janowczyk's insights on CAD were eye-opening, particularly in how algorithms assist clinicians in analyzing data from (f)MRI, histology, X-ray, CT, and more. He emphasized that CAD enhances efficiency and robustness in medical diagnoses by leveraging vast and continuously growing datasets. For instance, histology slides, which can be as large as 100,000 × 100,000 pixels and how CAD supports doctors by identifying trends and subtle image patterns which may not be visually discernible - providing decision support to pathologists rather than replacing them. This approach paves the way for precision medicine, where treatments are tailored to individual patients based on extensive retrospective evidence and data. The video will be out in July; stay tuned for more updates by following Saurish Speaks! #AI #MachineLearning #DeepLearning #DigitalPathology #Histology #ComputerAidedDiagnostics #HealthcareInnovation #SaurishSpeaks #AndrewJanowczyk #Pathology #PrecisionMedicine #MRI #CT #GB #Pixels #Patient #Hospital #Healthcare #Technology #Histology #Computer #CAD #EmoryUniversity #GeorgiaTech #Geneva #Data #July

Happy to share a new publication in the Journal of Imaging Informatics in Medicine (JIIM)! 🎉 This work, led by Michael Fei, explores self-supervised deep learning for body part regression and segmentation in whole-body imaging. Key findings include: * EfficientNet excels: Reduced error by 50% in body part regression. * Localized segmentation outperforms: Models trained on specific regions beat whole-body models. * Practical applications: Improved efficiency, scalability, and performance in medical imaging tasks. Excited about the potential of self-supervised learning to transform medical imaging! Read more here: https://lnkd.in/gRpzRJwF